Out-of-Sample Density Prediction of the End-of-Month Price of Crude Oil and the U.S. Economic Policy Uncertainty Index

What the paper says

Abstract Results reported in the recent studies of Conlon et al. (2022. “The Illusion of Oil Return Predictability: The Choice of Data Matters.” Journal of Banking & Finance 134: 106331) and Ellwanger and Snudden (2023. “Forecasts of the Real Price of Oil Revisited: Do They Beat the Random Walk?” Journal of Banking & Finance 154: 106962) have created a good deal of debate and controversy regarding the nature and extent of in- and out-of-sample crude oil price predictability when the end-of-month price is predicted instead of the monthly average price. At the same time, the newspaper-based U.S. economic policy uncertainty (EPU) index suggested in the study of Baker et al. (2016. “Measuring Economic Policy Uncertainty.” Quarterly Journal of Economics 131: 1593–636) has shown itself to be a very useful predictor of economic variables, such as equity returns and volatility. I assess the predictive impact of the log-EPU index on the end-of-month price of crude oil as opposed to the monthly average price. While the log-EPU index does not improve the relative accuracy of crude oil price return predictions, it has a statistically significant in- and out-of-sample predictive impact on the conditional distribution of the end-of-month log-crude oil price returns through the conditional volatility channel. The statistical evidence of relative predictability also translates into economic gains.